Informing Big Decisions: Analytical Generalizability for Large-Scale Evaluations.

Area of Science:

• Development Evaluation
• Organizational Learning
• Programmatic Generalizability

Background:

• Large-scale development evaluations face challenges in generalizability due to complex, multi-contextual, and multi-year project scopes.
• The Independent Evaluation Group (IEG) of the World Bank Group conducts evaluations to inform strategic decisions, ensure accountability, and synthesize evidence for future planning.

Purpose of the Study:

• To discuss the practical framework developed by the IEG to address generalizability in large-scale development evaluations.
• To outline how this framework meets methodological, institutional, and didactic needs for credible and actionable evaluation findings.

Main Methods:

• The article reflects on the IEG's practical framework for enhancing generalizability in complex evaluations.
• It examines the framework's application in bridging methodological principles with real-world evaluation challenges.
• Illustrative examples from IEG evaluations are used to demonstrate the framework's utility.

Main Results:

• The IEG framework provides a structured approach to enhance the generalizability of findings from large-scale, complex development evaluations.
• It addresses the need for valid findings across multiple levels of analysis and integrates multitiered mixed-methods approaches.
• The framework aids in synthesizing substantial evidence to identify key success or failure factors.

Conclusions:

• The IEG's practical framework offers a robust method for improving the generalizability of development evaluations.
• This framework can be applied by practitioners and researchers in diverse settings to enhance the credibility and utility of evaluation findings.
• Effective generalizability is crucial for informing significant organizational decisions and strategic planning in development initiatives.